Site Reliability Engineer

Site Reliability Engineer (SRE)

Location: Dubai

Duration: Permanent

We’re currently partnered with a leading technology consultancy who are scaling their tech team. They offer a diverse work environment that provide services in the UAE impacting millions of lives. We're currently helping them search for a Site Reliability Engineer to join their ever growing team.

Responsibilities:

• Architect, implement, and oversee scalable, high-performance AI and data infrastructure across cloud (AWS) and on-prem environments.
• Utilise automation tools (e.g., Terraform, Ansible) for provisioning, monitoring, and infrastructure optimisation.
• Design robust monitoring, alerting, and logging solutions to detect and mitigate potential failures before they impact operations.
• Develop and maintain seamless CI/CD pipelines to accelerate the deployment of AI models and data-driven applications.
• Optimise workflows to enhance efficiency, reduce deployment friction, and maintain system stability.
• Partner with AI researchers, data engineers, and developers to align infrastructure with project needs.
• Act as a bridge between AI, data, and infrastructure teams, ensuring smooth communication and technical alignment.
• Rapidly diagnose and resolve system incidents, conducting thorough root-cause analyses to prevent future issues.
• Establish and refine disaster recovery frameworks to safeguard AI and data assets.
• Implement stringent security protocols to protect AI and data infrastructure, ensuring compliance with industry regulations.
• Perform regular security evaluations, proactively addressing vulnerabilities.
• Identify opportunities to improve system scalability, efficiency, and resilience.
• Stay ahead of emerging trends in AI infrastructure, site reliability engineering, and cloud technologies.

Qualifications & skills:

• Bachelor’s or Master’s degree in Computer Science, Software Engineering, or a related field.
• 3-5 years experience in a similar role
• Experience with on-premise and cloud platforms (AWS, GCP, Azure) and container orchestration (Kubernetes, Docker).
• Experience with AI and data-specific infrastructure (e.g., GPU clusters, data lakes)
• Understanding of machine learning frameworks and data processing tools (e.g., TensorFlow, PyTorch, Apache Spark).